From a theoretical perspective, the output gap is probably the most comprehensive and convincing concept to describe the cyclical position of an economy. Unfortunately, for practical purposes, the concept depends on the determination of potential output, which is an inherently unobservable...

In this paper, we investigate whether the Google search activity can help in nowcasting the year-on-year growth rates of monthly US private consumption using a real-time data set. The Google-based forecasts are compared to those based on a benchmark AR(1) model and the models including the...

We systematically examine the comparative predictive performance of a number of alternative linear and non-linear models for stock and bond returns in the G7 countries. Besides Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STAR) regime switching...

Nowcasting has been a challenge in the recent economic crisis. We introduce the Toll Index, a new monthly indicator for business cycle forecasting and demonstrate its relevance using German data. The index measures the monthly transportation activity performed by heavy transport vehicles across...

This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions - from the economic effects of party control of government...

A framework for forecasting new COVID-19 cases jointly with hospital admissions and hospital beds with COVID-19 cases is presented. This project, dubbed CovidMod, produced 21-days ahead forecasts each working day from March 2021 to April 2022, and forecast errors that were used to assess...

We forecast economic activity in Argentina on a quarterly real-time basis using dynamic factors models (DFM) (Blanco et al. 2018) and evaluate their forecasting performance during the COVID19 pandemic of 2020. We compare the results of forecasts based on a pre-pandemic estimation of the...

We study how individual unemployment expectations are shaped and updated using a unique longitudinal survey data set with subjective unemployment expectations. The survey data is linked with third-party reported administrative data on unemployment realizations, such that we are able to examine...

In this paper we describe the newly developed System for Model Analysis in Real Time (SMART) used for forecasting and model analysis in Norges Bank. While the long-term goal is to include all empirical models used in forecasting in Norges Bank, the emphasis in this paper will be on the empirical...

We consider the effects of 'precision' screening policies for cancer guided by algorithms. We first show that complex machine learning models can indeed predict cancer better than simpler models that use established risk factors. We then tackle the evaluation challenge: an algorithm that can...